An Improved Task Allocation Strategy in Cloud using Modified K-means Clustering Technique
In the present era, cloud computing has earned much popularity, mainly because of its utilities and relevance with the current technological trends. It is an arrangement which is highly customizable and encapsulated for providing better computational services to its clients worldwide. In cloud compu...
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| Veröffentlicht in: | Egyptian informatics journal Jg. 21; H. 4; S. 201 - 208 |
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| Abstract | In the present era, cloud computing has earned much popularity, mainly because of its utilities and relevance with the current technological trends. It is an arrangement which is highly customizable and encapsulated for providing better computational services to its clients worldwide. In cloud computing, scheduling plays a pivotal role in the optimal utilization of resources. Prevalent priority based job scheduling strategies are silent in deciding scheduling scheme for tasks with the same priority and strive hard in appropriately allocating jobs to virtual machines. In the recent years, despite of much research in this field, these scheduling algorithms are unable to provide optimal solution and are lacking in one way or the other in their performance and efficiency. Work pertaining to the use of four criteria/credits for deciding priority, with modified K-means clustering technique is scant. Therefore, to eliminate the drawbacks of the prevalent or existing system and to enhance the performance and efficiency of cloud computing, a new credits based scheduling algorithm has been rendered. The proposed system considers four real time parameters/factors namely Task-Length, Task-Priority, Deadline and Cost, as credits and uses Modified K-means Clustering technique for categorizing the cloudlets and virtual machines (VMs). Results indicate that the suggested scheduling algorithm has excelled existing priority-based scheduling strategy and it has been empirically proven with experimental/simulated results in this paper. CloudSim 3.0.3, a Cloud Simulation Tool has been used to implement and test the proposed algorithm. |
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| AbstractList | In the present era, cloud computing has earned much popularity, mainly because of its utilities and relevance with the current technological trends. It is an arrangement which is highly customizable and encapsulated for providing better computational services to its clients worldwide. In cloud computing, scheduling plays a pivotal role in the optimal utilization of resources. Prevalent priority based job scheduling strategies are silent in deciding scheduling scheme for tasks with the same priority and strive hard in appropriately allocating jobs to virtual machines. In the recent years, despite of much research in this field, these scheduling algorithms are unable to provide optimal solution and are lacking in one way or the other in their performance and efficiency. Work pertaining to the use of four criteria/credits for deciding priority, with modified K-means clustering technique is scant. Therefore, to eliminate the drawbacks of the prevalent or existing system and to enhance the performance and efficiency of cloud computing, a new credits based scheduling algorithm has been rendered. The proposed system considers four real time parameters/factors namely Task-Length, Task-Priority, Deadline and Cost, as credits and uses Modified K-means Clustering technique for categorizing the cloudlets and virtual machines (VMs). Results indicate that the suggested scheduling algorithm has excelled existing priority-based scheduling strategy and it has been empirically proven with experimental/simulated results in this paper. CloudSim 3.0.3, a Cloud Simulation Tool has been used to implement and test the proposed algorithm. |
| Author | Sharma, Vrajesh Bala, Manju |
| Author_xml | – sequence: 1 givenname: Vrajesh surname: Sharma fullname: Sharma, Vrajesh email: vrajeshsharma@gmail.com, vrajesh.sharma@pu.ac.in organization: I. K. Gujral Punjab Technical University, Kapurthala Punjab, India – sequence: 2 givenname: Manju surname: Bala fullname: Bala, Manju organization: Khalsa College of Engineering & Technology, Amritsar, Punjab, India |
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| Keywords | Efficient Task Scheduling Strategies High performance network virtualization Categorization of Tasks Reduction in Total Computational Cost in Cloud Scheduling Algorithm in Cloud Scheduling Strategies in Cloud Computing Modified K-Means Task Allocation Strategies Rearrangement of Jobs in Cloud Multiple Credits Based Scheduling Categorization of Virtual Machines QoS Reducing Prediction Error in Cloud Computing Improving Makespan in Cloud Priority Based Scheduling Four Credits based Scheduling Efficiency Improvement in Cloud Mapping Cloudlets to VMs Virtualization Clustering Method |
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| References_xml | – reference: Assuncao, M. D., Netto, M. A. S., Koch, F., & Bianchi, S. (2012). Context-Aware Job Scheduling for Cloud Computing Environments. 2012 IEEE Fifth International Conference on Utility and Cloud Computing. doi:10.1109/ucc.2012.33 – reference: Liang Luo, Wenjun Wu, Dichen Di, Fei Zhang, Yizhou Yan, & Yaokuan Mao. (2012). A resource scheduling algorithm of cloud computing based on energy efficient optimization methods. 2012 International Green Computing Conference (IGCC). doi:10.1109/igcc.2012.6322251. – reference: Kaur, S., & Kalra, S. (2016). Disease prediction using hybrid K-means and support vector machine. 2016 1st India International Conference on Information Processing (IICIP). doi:10.1109/iicip.2016.7975367. – volume: 46 start-page: 913 year: 2015 end-page: 920 ident: b0030 article-title: Credit based scheduling algorithm in cloud computing environment publication-title: Procedia Comput. 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A Cost-Based Resource Scheduling Paradigm in Cloud Computing. 2011 12th International Conference on Parallel and Distributed Computing, Applications and Technologies. doi:10.1109/pdcat.2011.1 – reference: Ibrahim, E., El-Bahnasawy, N. A., & Omara, F. A. (2016). Task Scheduling Algorithm in Cloud Computing Environment Based on Cloud Pricing Models. 2016 World Symposium on Computer Applications & Research (WSCAR). doi:10.1109/wscar.2016.20. – ident: 10.1016/j.eij.2020.02.001_b0020 doi: 10.1109/CSC.2011.6138518 – ident: 10.1016/j.eij.2020.02.001_b0075 doi: 10.1109/PDCAT.2011.1 – ident: 10.1016/j.eij.2020.02.001_b0040 doi: 10.1109/IPDPS.2011.98 – year: 2012 ident: 10.1016/j.eij.2020.02.001_b0080 article-title: Dynamic slot-based task scheduling based on node workload in a MapReduce computation model publication-title: Anti-Counterfeiting, Secur. Identif. doi: 10.1109/ICASID.2012.6325318 – volume: 33 start-page: 942 year: 2012 ident: 10.1016/j.eij.2020.02.001_b0055 article-title: A new class of priority-based weighted fair scheduling algorithm publication-title: Phys. 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Sci. doi: 10.1016/j.procs.2015.02.162 – ident: 10.1016/j.eij.2020.02.001_b0090 doi: 10.1109/WSCAR.2016.20 – volume: 25 start-page: 673 issue: 3 year: 2014 ident: 10.1016/j.eij.2020.02.001_b0005 article-title: Improving network I/O virtualization for cloud computing publication-title: IEEE Trans. Parallel Distrib. Syst. doi: 10.1109/TPDS.2013.29 – ident: 10.1016/j.eij.2020.02.001_b0095 doi: 10.1109/IICIP.2016.7975367 – volume: 50 start-page: 778 year: 2012 ident: 10.1016/j.eij.2020.02.001_b0045 article-title: A priority based job scheduling algorithm in cloud computing publication-title: Procedia Eng. – ident: 10.1016/j.eij.2020.02.001_b0050 doi: 10.1109/CSC.2012.16 – ident: 10.1016/j.eij.2020.02.001_b0085 doi: 10.1109/IGCC.2012.6322251 – start-page: 637 year: 2015 ident: 10.1016/j.eij.2020.02.001_b0010 – volume: 48 start-page: 107 year: 2015 ident: 10.1016/j.eij.2020.02.001_b0025 article-title: Multi-objective tasks scheduling algorithm for cloud computing throughput optimization publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2015.04.158 |
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| SubjectTerms | Categorization of Tasks Categorization of Virtual Machines Clustering Method Efficiency Improvement in Cloud Efficient Task Scheduling Strategies Four Credits based Scheduling High performance network virtualization Improving Makespan in Cloud Mapping Cloudlets to VMs Modified K-Means Multiple Credits Based Scheduling Priority Based Scheduling QoS Rearrangement of Jobs in Cloud Reducing Prediction Error in Cloud Computing Reduction in Total Computational Cost in Cloud Scheduling Algorithm in Cloud Scheduling Strategies in Cloud Computing Task Allocation Strategies Virtualization |
| Title | An Improved Task Allocation Strategy in Cloud using Modified K-means Clustering Technique |
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